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Belief revision generalized: A joint characterization of Bayes' and Jeffrey's rules

Dietrich, Franz and List, Christian and Bradley, Richard (2016) Belief revision generalized: A joint characterization of Bayes' and Jeffrey's rules. Journal of Economic Theory, 162. pp. 352-371.

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Abstract

We present a general framework for representing belief-revision rules and use it to characterise Bayes's rule as a classical example and Jeffrey's rule as a non-classical one. In Jeffrey's rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes's rule, but a new assignment of probabilities to some events. Despite their differences, Bayes's and Jeffrey's rules can be characterized in terms of the same two axioms: "responsiveness", which requires that revised beliefs incorporate what has been learnt, and "conservativeness", which requires that beliefs on which the learnt input is "silent" do not change. (We give a precise technical definition of "silence".) To illustrate the use of non-Bayesian belief revision in economic theory, we sketch a simple decision-theoretic application.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Dietrich, Franz
List, Christian
Bradley, Richard
Keywords: Belief revision, Bayesian conditioning, Jeffrey conditioning, learning, decision theory, fine-grained versus coarse-grained beliefs, unawareness
Subjects: General Issues > Decision Theory
Specific Sciences > Economics
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
Depositing User: Christian List
Date Deposited: 25 Jun 2017 16:00
Last Modified: 25 Jun 2017 16:00
Item ID: 13154
Journal or Publication Title: Journal of Economic Theory
Official URL: https://doi.org/10.1016/j.jet.2015.11.006
DOI or Unique Handle: 10.1016/j.jet.2015.11.006
Subjects: General Issues > Decision Theory
Specific Sciences > Economics
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
Date: March 2016
Page Range: pp. 352-371
Volume: 162
URI: https://philsci-archive.pitt.edu/id/eprint/13154

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